Keynotes and Invited Talks
2022

Howard A. “Multifidelity Deep Operator Networks,” Workshop on Multifidelity DeepONets at Brown University Applied Mathematics CRUNCH Seminar. May 6, 2022. Virtual. (Invited Talk)

Howard, A. "High performance computing for multiphase flows," 2022 HPC Parallel Programming Workshop, Lehigh University, Bethlehem, PA , June 28, 2022. Virtual (Invited Talk)
2021
 Atzberger PJ. “Stochastic Immersed Boundary Methods,” Courant Institute, New York University, New York, NY, April 2021.
 Atzberger PJ. “Machine Learning for investigating dynamics of physical systems," PhILMs, DOE, May 2021.
 Bochev P. “Hybrid analyticnumerical compact models for radiationinduced photocurrent effects,” A symposium in honor of Jackie Chen’s selection as a 2020 DOE Office of Science Distinguished Scientist Fellow, Sandia National Laboratories, May 26, 2021.
 Daskalakis C. “Equilibrium Computation, GANs and the foundations of Deep Learning,” National Technical University of Athens, Greece, January 2021. (Invited Talk)
 Daskalakis C. “Equilibrium Computation, GANs and the foundations of Deep Learning,” Virtual Seminar Series on Games, Decisions and Networks, January 2021. (Invited Talk).
 Daskalakis C. “Equilibrium Computation and the foundations of Deep Learning,” AAAI Workshop on Reinforcement Learning in Games, February 2021. (Invited Talk).
 Daskalakis C. “Three ways Machine Learning fails and what to do about them,” NYIT School of Architecture and Design, February 2021. (Public Lecture).
 Daskalakis C. “Equilibrium Computation and the Foundations of Deep Learning,” 32nd International Conference on Algorithmic Learning Theory (ALT), March 2021. (Plenary Talk).
 Daskalakis C. “How AI fails, and why it matters,” Greek Scientists Society Symposium, March 2021. (Public Lecture).
 Daskalakis C. “The Revolution of Tomorrow and the moral implications of Artificial Intelligence,” Hellenic Innovation Network and Greek Consulate in Boston webinar, March 2021. (Panel Discussion).
 Daskalakis C. “How does Artificial Intelligence fail and what can we do about it?” Athens Science Virtual Festival, April 2021. (Public Lecture).
 Daskalakis C. “Robust (ML + MD) = Learned Mechanisms,” Google Market Algorithms Workshop, May 2021. (Invited Talk).
 Daskalakis C. “How Long Until Truly Intelligent Machines?” University of Crete, May 2021. (Public Lecture).
 Daskalakis C. “Equilibrium Computation and the Foundations of Deep Learning,” University of Washington, May 2021. (Invited Talk).
 Daskalakis C. “From von Neumann to Machine Learning: Equilibrium Computation and the Foundations of Deep Learning,” John von Neumann Lecture, University of Zurich and ETH, June 2021. (Public Talk).
 Daskalakis C. “Equilibrium Computation and Deep Learning,” CVPR conference, June 2021. (Keynote Talk).
 Daskalakis C. “MinMax Optimization: from von Neumann to Deep Learning, Nash and Wilson”, Stony Brook Game Theory Festival, July 2021. (Plenary Talk).
 Daskalakis C. “MinMax Optimization: from von Neumann to Deep Learning,” Conference on Research on Economic Theory and Econometrics, July 2021, Naxos, Greece. (Plenary Talk).
 Daskalakis C. “Equilibrium Computation and Machine Learning,” Congress of the Game Theory Society, July 2021. (SemiPlenary Talk).
 Daskalakis C. “MinMax Optimization: from von Neumann to Deep Learning,” Symposium on Fundamentals of Computation Theory, September 2021. (Plenary Talk).
 D’Elia M. “Challenges in nonlocal modeling: nonlocal boundary conditions and nonlocal interfaces,” WCCMECCOMAS 2020, January 1115, 2021.
 D’Elia M. “Data driven learning of nonlocal models: from MD to continuum mechanics,” NM Machine Learning in Material Science Symposium, February 23, 2021.
 D’Elia M. “A General Framework for Nonlocal Domain Decomposition,” SIAM Computational Science and Engineering Conference, March 2021. (Invited Talk).
 D’Elia M. “Data Driven Learning of Nonlocal Models,” Computing and Mathematical Science Colloquium at the California Institute of Technology, March 10, 2021. (Invited Talk).
 D’Elia M. “Data Driven Learning of Nonlocal Models: from High Fidelity Simulations to Constitutive Laws,” AAAI 2021 Spring Symposium on Combining Artificial Intelligence and Machine Learning with Physics Sciences, March 2224, 2021. (Invited Talk).
 D’Elia M. “Data Driven Learning of Nonlocal Models,” CNA seminar at Carnegie Mellon University, March 16, 2021. (Invited Talk).
 D’Elia M. “Data Driven Learning of Nonlocal Models,” Mathematics Department Colloquium at Florida State University, March 26, 2021. (Invited Talk).
 D’Elia M. “Data driven learning of nonlocal models,” The 50th John H. Barrett Memorial Lectures, May 1719, 2021. (Keynote).
 D’Elia M. “A new variableorder fractional Laplacian,” SIAM MS 21, May 1728, 2021.
 D’Elia M. “Addressing microscale interfaces via nonlocal models using machine learning,” Coupled Problems 21, June 1416, 2021.
 D’Elia M. “Data driven learning of nonlocal models,” ALOP Workshop, Nonlocal Models: Analysis, Optimization, and Implementation, July 1214, 2021. (Plenary).
 D’Elia M. “A unified theory of fractional and nonlocal calculus,” INdAM workshop on Fractional Differential Equations: Modeling, Discretization, and Numerical Solvers, July 1214, 2021. (Plenary)
 D’Elia M. "Nonlocal Model Learning: from Highfidelity Simulations to Nonlocal Constitutive Laws,” ALOP Workshop on nonlocal models. July 12, 2021, Trier, Germany. (Plenary talk)
 D’Elia M. “A Unified Theory of Fractional and Nonlocal Calculus and its Consequences on Nonlocal Model Discovery," INdAM Workshop on Fractional Differential Equations. July 13, 2021, Rome, Italy. (Plenary talk)
 D’Elia M. “Being a mathematician at a National Laboratory,” REU/RET Panel on Careers in Data Science at the Emory University, July 19, 2021. (Plenary).
 D’Elia M. “Data driven learning of nonlocal models,” SIAM AN 21. July 22, 2021.
 D’Elia M. “Data driven learning of nonlocal models,” 16th U.S. National Congress on Computational Mechanics. July 28, 2021.
 D’Elia M. “Data driven learning of nonlocal models,” DDPS Seminar at Lawrence Livermore National Laboratory, July 30, 2021.
 D’Elia M. “Datadriven learning of nonlocal models: bridging scales with nonlocality,” Mechanistic Machine Learning and Digital Twins for Computational Science, Engineering & Technology, September 2021.
 D’Elia M. “Datadriven learning of nonlocal models: bridging scales with nonlocality,” Machine learning in heterogeneous porous materials, AmeriMech Symposium Series, October 46, 2021.
 D’Elia M. "Recent advances in nonlocal modeling and learning,” University of Whuan. October 26, 2021. (Invited Talk)
 Gulian M. “Datadriven learning of nonlocal physics from highfidelity synthetic data,” CONFERENCIA IFIP TC7 2021, August 30, 2021.
 Gulian M. “Analysis of Anisotropic Nonlocal Diffusion Models: Wellposedness of Fractional Problems for Anomalous Transport,” SIAM MS 21, May 26, 2021.
 Gulian M. “A block coordinate descent optimizer for classification problems exploiting convexity,” AAAIMLPS, March 3, 2021.
 Gulian M. “Robust architectures, initialization, and training for deep neural networks via the adaptive basis interpretation,” SIAM SEAS, September 18, 2021.
 Howard A. “Nonlocal models for modeling multiphase fluids,” Arizona State University, Tempe, AZ, 2021.
 Howard A. “Nonlocal models for modeling multiphase fluids,” San Diego State University, San Diego, CA, 2021.
 Howard A. “Nonlocal models for modeling multiphase fluids,” University of Washington, Seattle, WA, 2021.
 Howard A. “Two multifidelity approaches for machine learning,” RAMSES: Reduced order models; Approximation theory, Machine Learning; Surrogates, Emulators and Simulators, Trieste Italy (online), December 2021. (Invited Talk).
 Karniadakis GE. "“PhysicsInformed Neural Networks PINNs and DeepOnet: Theory and Applications,” Siemens Inc., 2021.
 Karniadakis GE. "“PhysicsInformed Neural Networks PINNs and DeepOnet: Theory and Applications,” Hitachi Inc., 2021.
 Karniadakis GE. "“PhysicsInformed Neural Networks PINNs and DeepOnet: Theory and Applications,” Bosch Inc., 2021.
 Karniadakis GE. "“PhysicsInformed Neural Networks PINNs and DeepOnet: Theory and Applications,” University of Cambridge., 2021.
 Karniadakis GE. "“PhysicsInformed Neural Networks PINNs and DeepOnet: Theory and Applications,” AMD Inc., 2021.
 Karniadakis GE. "“PhysicsInformed Neural Networks PINNs and DeepOnet: Theory and Applications,” 10th Workshop on ParallelinTime Integration, 2021. (Plenary Talk).
 Parks M. “nPINNS: Nonlocal PhysicsInformed Neural Networks,” One Nonlocal World, January 23, 2021.
 Parks, M. “nPINNS: Nonlocal PhysicsInformed Neural Networks,” SIAM Computational Science and Engineering Conference, March 2021. (Invited Talk).
 Parks M. “Computational Aspects of Nonlocal Models,” Center for Nonlinear Analysis, Department of Mathematical Sciences MCS, Carnegie Mellon University, April 13, 2021.
 Parks M. "“nPINNS: Nonlocal PhysicsInformed Neural Networks,” 16th U.S. National Congress on Computational Mechanics, Chicago, IL, July 29, 2021. (Invited Talk).
 Parks M. “On Neumanntype Boundary Conditions for Nonlocal Models,” Mechanistic Machine Learning and Digital Twins for Computational Science, Engineering & Technology (MMLDTCSET), San Diego, California, September 2021. (Invited Talk).
 Perego M. “Modeling land ice with deep operator networks,” SIAM Southeastern Atlantic Section Conference. Sept 19, 2021.
 Patel R. “Control volume PINNs: a method for solving inverse problems with hyperbolic PDEs,” Brown University CRUNCH Seminar, January 2021.
 Patel R. “A PhysicsInformed Operator Regression Framework for Extracting DataDriven Continuum Models,” SIAM Conference on Computational Science and Engineering, March 2021.
 Patel R. “Modal operator regression for extracting nonlocal continuum models,” 16th U.S. National Congress on Computational Mechanics, July 2021.
 Stinis, P. “Machinelearning Enhanced Perturbative Renormalization,” SIAM Computational Science and Engineering Conference, March 15, 2021. (Invited Talk).
 Stinis, P. “Machinelearning enhanced perturbative renormalization,” SIAM Conference on Applications of Dynamical Systems (DS21), May 2327, 2021. (Invited Talk).
 Stinis, P. “A spectral approach for timedependent PDE using machinelearned basis functions.” University of Pennsylvania, Applied Mathematics Seminar. December 2021. (Invited Talk)
 Trask N. “Designing convergent and structure preserving architectures for SciML,” UTEP Department Webinar, February 26, 2021.
 Trask N. “A datadriven exterior calculus for model discovery,” SIAM CSE, March 1, 2021.
 Trask N. “Designing convergent and structure preserving architectures for SciML,” One World ML virtual webinar, March 3, 2021.
 Trask N. “Physicsinformed ML tutorial for Northwestern engineering,” Northwestern Engineering colloquium, March 5, 2021
 Trask N. “A datadriven exterior calculus for model discovery,” USACM UQ Webinar, March 18, 2021.
 Trask N. “Making physicsinformed ML work,” Los Alamos invited machine learning webinar, March 17, 2021.
 Trask N. “Partition of unity networks: deep hpapproximation,” AAAI MLPS virtual meeting, March 17, 2021.
 Trask N. “Structure preservation and mathematical foundations for scientific machine learning,” CIS External Review, March 24, 2021
 Trask N. “Structure preserving architectures for SciML,” CRUNCH webinar Brown University, June 7, 2021.
 Trask N. “Structure preserving machine learning for highconsequence engineering and science applications,” New Research Ideas Forum (SNL), June 17, 2021.
 Trask N. “A datadriven exterior calculus for model discovery,” USACM, July 27, 2021.
 Trask N. “Discovery of structurepreserving finite element spaces for multiscale,” Mechanistic Machine Learning and Digital Twins for Computational Science, Engineering & Technology, September 27, 2021.
 Trask N. “A datadriven exterior calculus for model discovery,” RPI engineering webinar, September 20, 2021.
 Valiant G. “Charting the Landscape of Memory/Data Tradeoffs in Continuous Optimization: A Survey of Open Problems,” Simons Institute for Theory of Computing, workshop on Rigorous Evidence for Information Computation Tradeoffs, September, 2021.
 Valiant G. “Estimation and Learning Beyond the IID Setting,” Workshop MHC2020: Mixtures, Hidden Markov Models and Clustering, June, 2021.
 Valiant G. “Statistical Challenges in the Federated Setting,” New Problems and Perspectives on Sampling, Learning, and Memory, April, 2021
2020
 Atzberger P.J. “Geometric Approaches for Machine Learning in the Sciences and Engineering,” University of California, Davis, May 2020. (Invited Talk).
 Bochev P.B. "What does a computational scientist do at a national lab," Casper College, November 19, 2020. (Invited Talk).
 Daskalakis, C. “Statistical Inference from Dependent Observations,” National Technical University of Athens, Athens, Greece, January 2020. (Invited Talk).
 Daskalakis, C. “Statistical Inference from Dependent Observations,” Institute for Advanced Studies Computer Science/Discrete Mathematics Seminar, Princeton, NJ, March 2020. (Invited Talk).
 Daskalakis, C. “MinMax Optimization and Deep Learning,” Institute for Advanced Studies Special Year in Optimization, Statistics, and Theoretical Machine Learning Seminar, Princeton, NJ, March 2020. (Invited Talk).
 Daskalakis, C. “Robust Learning from Censored Data,” MITMicrosoft Research Trustworthy and Robust AI Collaboration Workshop, Cambridge, MA, June 2020. (Invited Talk).
 Daskalakis, C. “The Complexity of MinMax Optimization,” Universit´e de Montreal Machine LearningOptimization Seminar, Montreal, Canada, July 2020. (Invited Talk).
 Daskalakis, C. “Learning from Biased Data,” MIT Brains, Minds, and Machines Summer Course, Cambridge, MA, August 2020. (Invited Lecture).
 Daskalakis, C. “How does Machine Learning fail, and what to do about it?,” ERC organized session on “Artificial Intelligence: A blessing or a threat for society?” at EuroScience Open Forum (ESOF), Trieste, Italy, September 2020. (Invited Talk and Panel).
 D’Elia M. “Nonlocal models in computational Science and Engineering,” University of New Mexico, Albuquerque, NM, February 2020. (Invited Lecture)
 D’Elia M. "Nonlocal Models in Computational Science and Engineering," GA Scientific Computing Symposium, Emory University, Atlanta, GA, February 29, 2020. (Invited Talk).
 D’Elia M, M Gulian, G Pang, M Parks, and G Karniadakis. "A Unified Theoretical and Computational Nonlocal Framework: Generalized Nonlocal Calculus and PhysicsInformed Neural Networks," Recent Progress in Nonlocal Modeling, Analysis and Computation, Beijing, China, June 16, 2020. (Invited Talk).
 D’Elia, M. “A Unified Theory of Fractional and Nonlocal Vector Calculus,” Brown University, August 2020. (Invited Lecture)
 D’Elia, M. A unified theoretical and computational nonlocal framework: Generalized vector calculus and machinelearned nonlocal models," CMAI (Center for Mathematics and Artificial Intelligence), George Mason University, Fairfax, VA, August 7, 2020. (Invited Talk).
 D’Elia M. “A unified theoretical and computational nonlocal framework: generalized vector calculus and machinelearnt nonlocal models,” SIAM TXLA Section. October 1718, 2020.
 D’Elia M. “A unified, datadriven framework for the identification of nonlocal models: ALGORITHMS & APPLICATIONS,” Engineering Sciences Seminar at Sandia National Laboratories. December 10, 2020. Virtual.
 D’Elia M., “A unified theoretical and computational nonlocal framework,” Mathematics Department Colloquium at MODEMAT, Ecuador. December 15, 2020.
 He, Q. “Machine Learning Enhanced Computational Mechanics,” SE Special Seminar in Computational Mechanics, Department of Structural Engineering at University of California San Diego, La Jolla, California, March 2020. (Invited Talk).
 Patel R. “PDE discovery with machine learning,” University of New Mexico Applied Math Seminar, November 2020.
 Stinis, P. “Enforcing constraints for time series prediction in supervised, unsupervised and reinforcement learning,” SIAM Conference on Mathematics of Data Science (MDS20), Cleveland, Ohio, June 2020. (Invited Talk).
 Stinis, P. “Enforcing constraints for time series prediction in supervised, unsupervised and reinforcement learning,” SIAM/CAIMS Annual Meeting (AN20), Toronto, Canada, July 2020. (Invited Talk).
 Trask, N. “Compatible meshfree discretization,” UIUC civil engineering colloquium, UrbanaChampaign, IL, February 2020. (Invited Talk)
 Trask N. “A datadriven exterior calculus for model discovery,” Princeton Plasma Physics Laboratory Webinar, November 16, 2020.
 Trask N. “ASCR physicsinformed machine learning at SNL,” Presentation to DOE Office of AI, December 10, 2020.
 Valiant G. "Constrained Learning," Information Theory and Applications (ITA), San Diego, CA, February 2020. (Plenary Session).
 Valiant G. "Randomly Collected, Worst Case Data," Workshop on Local Algorithms (WOLA), July 2020. (Plenary Talk).
 Valiant G. “Statistical Challenges in the Federated Setting,” Federated Learning One World Seminar (FLOW), November, 2020.
 Valiant G. “WorstCase Analysis for Randomly Collected Data,” University of Wisconsin, Madison, October, 2020. (Invited Talk)
2019
 Bochev PB “Mimetic meshfree methods or how to be compatible without a mesh,” Conference on Computational Mathematics and Applications, Las Vegas, NV. October 2019. (Invited Talk).
 Daskalakis C. Open Data Science Conference, Boston, MA, April 2019. (Keynote).
 Daskalakis C. ACM Summer School on Data Science, Athens, Greece, July 2019. (Keynote).
 Daskalakis C. Workshop on Algorithms for Learning and Economics, Rhodes, Greece, July 2019. (Invited Talk).
 Daskalakis C. RANDOMAPPROX Conference, Cambridge, MA, September 2019. (Keynote).
 Daskalakis C. H2O AI World New York, New York, NY, October 2019. (Keynote).
 Daskalakis C. Inference on Graphical Models Conference, Columbia University, New York, NY, October 2019. (Invited Talk).
 D’Elia M. “Nonlocal models in computational Science and Engineering: challenges and applications,” University of California at Berkeley, Berkely, CA, November 2019. (Invited Talk).
 He Q. “Machine Learning Enhanced Computational Mechanics: ReducedOrder Modeling and PhysicsInformed DataDriven Computing,” Sonny Astani Civil and Environmental Engineering Seminar, University of Southern California, Los Angeles, California, November 2019. (Invited Talk).
 Karniadakis GE. "PhysicsInformed Neural Networks (PINNs)," Machine Learning in Heliophysics, Amsterdam, Netherlands, Sept 1620, 2019. (Keynote).
 Karniadakis GE. "Physicsinformed neural networks (PINNs) with uncertainty quantification," FrontUQ19: Workshop on Frontiers of Uncertainty Quantification in Fluid Dynamics, Pisa, Italy, Sept 1113, 2019. (Keynote)
 Karniadakis GE. "Uncertainty Quantification for Physics Informed Neural Networks," UNCECOMP: International Conference on Uncertainty Quantification in Computational Sciences and Engineering, Crete, Greece, June 2426, 2019. (Keynote)
 Karniadakis GE. "PhysicsInformed Learning Machines for Physical Systems," CFD IMPACT Conference, D. Dan and Betty Kahn Mechanical Engineering Building Technion – Israel Institute of Technology, Haifa, Israel, July 1, 2019. (Keynote)
 Karniadakis GE. 22nd Korean SIAM Conference, Seoul, Korea, May 1718, 2019. (Keynote)
 Karniadakis GE. "PhysicsInformed Neural Networks (PINNs) for solving stochastic and fractional PDEs," Machine Learning for Multiscale Model Reduction Workshop, Harvard University, Cambridge, MA, March 2729, 2019. (Keynote).
 Trask N. ”Compatible meshfree discretization,” Tufts university applied mathematics colloquium, Medford, MA, December 2019. (Invited Talk).
 Xu K, E Darve, and D Huang. "Physics informed machine learning," 15th U.S. National Congress of Computational Mechanics, Austin, TX, July 28Aug 1, 2019. (Keynote).
2018
 Daskalakis C. NIPS 2018 Workshop on "Smooth Games Optimization and Machine Learning," Montreal, Canada, December 2018. (Invited Talk).